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Credible interval estimates for official statistics with survey nonresponse

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  • Manski, Charles F.

Abstract

Government agencies commonly report official statistics based on survey data as point estimates, without accompanying measures of error. Agencies could measure sampling error using established statistical principles, but it is more challenging to measure nonsampling errors. This paper considers error due to survey nonresponse. The standard practice has been to use weights and imputations to implement assumptions that nonresponse is conditionally random. I review modern research deriving interval estimates that make no assumptions about the values of missing data. To demonstrate the implications for official statistics, I use data from the U.S. Current Population Survey to form interval estimates for median household income, the family poverty rate, and the unemployment rate. I then explore some of the middle ground between interval estimation making no assumptions and point estimation assuming that nonresponse is conditionally random.

Suggested Citation

  • Manski, Charles F., 2016. "Credible interval estimates for official statistics with survey nonresponse," Journal of Econometrics, Elsevier, vol. 191(2), pages 293-301.
    • Handle: RePEc:eee:econom:v:191:y:2016:i:2:p:293-301
    DOI: 10.1016/j.jeconom.2015.12.002
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    References listed on IDEAS

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    1. Charles F. Manski, 2013. "Response to the Review of ‘Public Policy in an Uncertain World’," Economic Journal, Royal Economic Society, vol. 0, pages 412-415, August.
    2. Patrick Kline & Andres Santos, 2013. "Sensitivity to missing data assumptions: Theory and an evaluation of the U.S. wage structure," Quantitative Economics, Econometric Society, vol. 4(2), pages 231-267, July.
    3. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444606, September.
    4. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    5. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    6. Jörg Stoye, 2010. "Partial identification of spread parameters," Quantitative Economics, Econometric Society, vol. 1(2), pages 323-357, November.
    7. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
    8. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444590, September.
    9. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
    10. Manski, Charles F., 2013. "Public Policy in an Uncertain World: Analysis and Decisions," Economics Books, Harvard University Press, number 9780674066892, Spring.
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    Citations

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    Cited by:

    1. Ana Beatriz Galvão & James Mitchell, 2024. "Communicating Data Uncertainty: Multiwave Experimental Evidence for UK GDP," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(1), pages 81-114, February.
    2. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    3. repec:wrk:wrkemf:30 is not listed on IDEAS
    4. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    5. repec:wrk:wrkemf:35 is not listed on IDEAS
    6. Mazzi Gian Luigi & Mitchell James & Carausu Florabela, 2021. "Measuring and Communicating the Uncertainty in Official Economic Statistics," Journal of Official Statistics, Sciendo, vol. 37(2), pages 289-316, June.
    7. Ana Beatriz Galvão & James Mitchell & Johnny Runge, 2019. "Communicating Data Uncertainty: Experimental Evidence for U.K. GDP," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-20, Economic Statistics Centre of Excellence (ESCoE).
    8. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.
    9. repec:wrk:wrkemf:22 is not listed on IDEAS
    10. repec:wrk:wrkemf:24 is not listed on IDEAS
    11. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.
    12. Charles F. Manski, 2022. "Inference with Imputed Data: The Allure of Making Stuff Up," Papers 2205.07388, arXiv.org.
    13. Rami V. Tabri & Mathew J. Elias, 2024. "Testing for Restricted Stochastic Dominance under Survey Nonresponse with Panel Data: Theory and an Evaluation of Poverty in Australia," Papers 2406.15702, arXiv.org.
    14. Fakih, Ali & Makdissi, Paul & Marrouch, Walid & Tabri, Rami V. & Yazbeck, Myra, 2022. "A stochastic dominance test under survey nonresponse with an application to comparing trust levels in Lebanese public institutions," Journal of Econometrics, Elsevier, vol. 228(2), pages 342-358.
    15. Andridge Rebecca R. & Little Roderick J.A., 2020. "Proxy Pattern-Mixture Analysis for a Binary Variable Subject to Nonresponse," Journal of Official Statistics, Sciendo, vol. 36(3), pages 703-728, September.

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